Data Scientist

🕒 July 29

🇺🇸 United States – Remote

💵 $140k - $165k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

📊 Data Scientist

👻 Ghost score 6%

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ScienceLogic

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2003

🤖 Artificial Intelligence

☁️ SaaS

🤝 B2B

💰 $21.2M Venture Round - ScienceLogic on 2022-10

Artificial Intelligence • SaaS • B2B

ScienceLogic is a provider of AI-driven observability and AIOps software that helps organizations observe, automate, and troubleshoot complex hybrid IT environments. Its ScienceLogic AI Platform (branded Skylar) combines hybrid cloud monitoring, network and application observability, automated root-cause analysis, workflow automation, compliance and configuration management, and extensive integrations to reduce mean time to repair (MTTR) and consolidate IT tools. The company delivers its platform as a SaaS solution (with rapid deployment options), targets enterprise and service-provider IT operations, and emphasizes AI/automation to drive operational efficiency, resiliency, and cost reduction.

📋 Description

• Design and own evaluation harnesses for LLM and agentic outputs — golden sets, regression suites, and rubric-based scoring. • Build and calibrate LLM-as-judge pipelines; validate judges against human labels and control for their bias and variance. • Define and track response-quality metrics: faithfulness/groundedness, hallucination rate, answer relevance and completeness, instruction-following, and persona adherence. • Curate, version, and grow evaluation datasets as the product and its surfaces evolve. • Benchmark the models in the suite against each other to decide which model handles which task, and quantify the quality cost of running smaller, local models versus larger alternatives. • Red-team the system: prompt injection, jailbreaks, tool-misuse, and edge-case discovery. • Design chaos and stress tests that probe model and agent reliability under degraded or hostile conditions. • Characterize failure modes and feed them back into guardrails and regression coverage. • Evaluate retrieval quality over the document corpus — recall@k, MRR/nDCG, context precision and recall — and run experiments on chunking, indexing, and hybrid retrieval strategies. • Analyze multi-step agent trajectories: tool-call correctness, trajectory efficiency, replayable-state inspection, and guardrail-breach behavior. • Assess intent classification and routing quality as measurable components, not black boxes. • Build standing evaluation that catches quality and behavioral regressions when a model in the suite is swapped, upgraded, or re-quantized, or when prompts and pipelines change. • Monitor output-distribution and quality drift in production; distinguish genuine regressions from noise on stochastic outputs. • Recommend and validate fixes through the levers available with local models — prompt changes, retrieval and grounding adjustments, routing changes, or model selection. • Build, ship, and own production models that forecast and surface trends from operational telemetry — capacity and resource forecasting, anomaly prediction, and early-warning signals on metrics and logs. • Take these from prototype to production and keep them healthy: deployment, monitoring, recalibration, and retraining as data and behavior shift. • Define accuracy and lead-time metrics that matter operationally — precision/recall on predicted incidents, forecast error, how far ahead a signal fires — not just offline scores. • Wire predictive signals into the LLM and agentic layer so forecasts and trends feed reasoning, advisories, and operator-facing recommendations.

🎯 Requirements

• Bachelor's or Master's in Data Science, Computer Science, Statistics, Mathematics, or a related field or equivalent experience. • 3+ years in data science, ML, or applied quantitative analysis. • Strong applied statistics, with the judgment to design sound experiments and significance tests on noisy, non-deterministic outputs (not just clean A/B conversion). • Experience building, deploying, and monitoring predictive or time-series models in production: forecasting, anomaly detection, or trend analysis, including recalibration as data shifts. • Demonstrated work evaluating, analyzing, or improving LLM or NLP systems: eval design, quality measurement, retrieval evaluation, or agent analysis. • Proficiency in Python. • Strong SQL and comfort querying large analytical datasets. • Fluency with foundation models and hands-on experience with the modern LLM evaluation and tooling layer — eval/harness frameworks, judge pipelines, and the libraries used to serve, prompt, and test models. • Ability to build analysis and visualization in code.

🏖️ Benefits

• Comprehensive medical, dental and vision plans. • 401(k) plan with employer match. • Flexible Paid Time Off (FTO) so that you can take the time that you need to re-energize. • Volunteer Time Off (VTO) - take two days off per calendar year to volunteer with your preferred charitable organization. • 5-year Service Milestone Sabbatical. • Paid parental leave. • Generous employee referral bonus program. • Pet insurance. • HQ Office centrally located in Reston Town Center featuring a well-stocked kitchen with rotating snacks and beverages, and catered lunch on Thursdays. • Regular virtual company-wide events, including cooking classes, yoga, meditation and more. • The opportunity to learn and develop from some of the best and brightest minds in the industry!